Utilization of physician services for diabetic patients from ethnic minorities
Bibliographic record
Abstract
BACKGROUND: Diabetes is a common chronic disease, which results in significant morbidity and mortality. Although ethnic variations in disease prevalence are known, variations in the utilization of physician services for the disease (particularly in publicly funded health care systems) are uncertain. METHODS: Self-reported ethnicity was determined from two population health surveys in Ontario, Canada. These data were linked to administrative data sources, including an administrative data-derived disease registry. Diabetes prevalence was determined for each ethnic group. Utilization of physician services for primary care, diabetes specialist care and eye examinations was compared among ethnic groups, adjusting for age, sex, socioeconomic status and diabetes duration. RESULTS: There were 20,788 eligible survey respondents. Standardized diabetes prevalence was elevated for the South Asian and Black populations (11.1 and 11.0%, respectively) compared with that for the White population (5.9%). Ethnic minorities with diabetes were less likely to receive an eye examination compared with White patients (adjusted OR, 0.63; 95% CI, 0.46-0.85). The use of primary care and diabetes specialist care did not differ. CONCLUSION: Ethnic minorities with diabetes are less likely to receive eye examinations. This disparity in quality of care could lead to worse clinical outcomes for these patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".